Import Library and Setup

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(lubridate)

## 
## Attaching package: 'lubridate'

## The following objects are masked from 'package:base':
## 
##     date, intersect, setdiff, union

library(forecast)

## Registered S3 method overwritten by 'quantmod':
##   method            from
##   as.zoo.data.frame zoo

library(TTR)
library(ggplot2)
library(tseries)
library(gridExtra)

## 
## Attaching package: 'gridExtra'

## The following object is masked from 'package:dplyr':
## 
##     combine

Data Import

covid <- read.csv("D:/emotions/archive (1)/covid_19_indonesia_time_series_all.csv")
rmarkdown::paged_table(covid)

The data has 4,578 obs and 37 columns.

#Data Preparation and EDA

#subset the location only Jakarta, and column i..Date, and New.Cases and change data type
covid1 <- covid %>% 
  subset(Location == "DKI Jakarta") %>% 
  select(ï..Date, New.Cases) %>% 
  mutate(ï..Date = mdy(ï..Date)) 

#change the column name from i..Date to Date
colnames(covid1)[1] = "Date"

#After subset the data can’t contain NA (missing value)

#Check if there is missing column in data
colSums(is.na(covid1))

##      Date New.Cases 
##         0         0

The data in Maret is so much of 0 value, so we’ll use the data start from April..The data must be ordered by time.

covid1 <- covid1 %>% 
  subset(Date >= "2020-04-01")

Then we’ll check when the data start and end.

range(covid1$Date)

## [1] "2020-04-01" "2021-11-05"

Now, our data is from April 1, 2020 to July 31, 2020. Then we’ll convert the data to ts object.

#create object ts 
covid_ts <- ts(data = covid1$New.Cases,
   start = min(covid1$Date),
   frequency = 7) #weekly seasonality 

#visualise object covid_ts
covid_ts %>% autoplot()

From the plot, we can see that our data’s model is multiplicative. We’ll try to see the seasonality, trend and error of data.

covid_dc <- decompose(covid_ts, type = "multiplicative")
covid_dc %>% autoplot()

We can also see an up or down trend using adjusted seasonal which has removing the effects of seasonal data.

autoplot(covid_dc$x - covid_dc$seasonal)

From the plot and information above, we can see that the data’s seasonal pattern is random or no seasonal. The trend patterns of data is increasing.